R Notebook

This is an R Markdown Notebook. When you execute code within the notebook, the results appear beneath the code.

Try executing this chunk by clicking the Run button within the chunk or by placing your cursor inside it and pressing Ctrl+Shift+Enter.

source("tianfengRwrappers.R")
ds2 <- readRDS("ds2.rds")
selected_features <- read.csv("./datatable/ds2_features.csv", stringsAsFactors = F)
selected_features <- selected_features$Feature
Idents(ds2) <- ds2$seurat_clusters
ggobj <- multi_featureplot(selected_features[1:9], ds2, labels = NULL)
ggsave("ds2_features.png", device = png, height = 8, width = 8, plot = ggobj)

umap plot

ggsave("./fig2/ds2_ACumap.png", device = png, height = 4, width = 6, 
       plot = umapplot(ds2_AC, group.by = "Classification1", label.size = 5))

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the
existing scale.

ggsave("./fig2/ds2_PAumap.png", device = png, height = 4, width = 6, 
       plot = umapplot(ds2_PA, group.by = "Classification1", label.size = 5))

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the
existing scale.

umapplot(ds2_AC,) umapplot(ds2_PA, group.by = “Classification1”)

Where is the Fibromyocyte

LUM

sep = 0.3
surfaceplot2("LUM",ds2,x_seq = seq(-2,13, sep), y_seq = seq(2,17,sep))

addmodulescore

geneset <- read.table("SMC")

ds2_AC <- AddModuleScore(ds2_AC,features = geneset, name = 'SMC_score')
ds2_PA <- AddModuleScore(ds2_PA,features = geneset, name = 'SMC_score')

(f("SMC_score1",label = F, ds2_AC) + scale_colour_gradient(low="#1E90FF", high="#ff2121")) %>%
  ggsave("./fig2/ds2_AC_SMCscore.png", device = png, height = 4, width = 5, plot = .)
(f("SMC_score1",label = F, ds2_PA) + scale_colour_gradient(low="#1E90FF", high="#ff2121")) %>%
  ggsave("./fig2/ds2_PA_SMCscore.png", device = png, height = 4, width = 5, plot = .)


# dataset1 <- AddModuleScore_UCell(dataset1,features = geneset, name = 'fibromyo_score')


geneset <- read.table("FB")

ds2_AC <- AddModuleScore(ds2_AC,features = geneset, name = 'FB_score')
ds2_PA <- AddModuleScore(ds2_PA,features = geneset, name = 'FB_score')

(f("FB_score1", label = F, ds2_AC) +scale_colour_gradient(low="#1E90FF", high="#ff2121")) %>%
  ggsave("./fig2/ds2_AC_FBscore.png", device = png, height = 4, width = 5, plot = .)
(f("FB_score1", label = F, ds2_PA) +scale_colour_gradient(low="#1E90FF", high="#ff2121")) %>%
  ggsave("./fig2/ds2_PA_FBscore.png", device = png, height = 4, width = 5, plot = .)

key features have gradient expression

BGN LUM

ridgetheme <- theme(plot.title = element_text(size = 15,color="black",hjust = 0.5),
                 axis.title = element_text(size = 15,color ="black"), 
                 axis.text = element_text(size = 15,color = "black"),
                 panel.grid.minor.y = element_blank(),
                 panel.grid.minor.x = element_blank(),
                 axis.text.x = element_text(angle = 0, hjust = 1),
                 panel.grid=element_blank(),
                 legend.position = "top",
                 legend.text = element_text(size= 15),
                 legend.title= element_text(size= 15)) 

ridge plot

df <- FetchData(ds2_AC,vars = c("FB_score1","SMC_score1","BGN","LUM","UMAP_1","UMAP_2"))
# df <- arrange(df,FB_score1,by_group = F)
data <- cbind(df,index = 1:nrow(df),cluster = Idents(ds2_AC))

(ggplot(data,aes(x=SMC_score1)) + geom_point(aes(y = BGN, color = cluster),alpha = 1) + geom_smooth(aes(y = BGN), color = "red") + theme_classic() + ridgetheme + scale_y_continuous(limits = c(1,5)) + scale_color_manual(values = colors_list) + theme(legend.key.size = unit(1,"cm")) + guides(colour = guide_legend(override.aes = list(size=10)))) 

(ggplot(data,aes(x=FB_score1)) + geom_point(aes(y = LUM, color = cluster),alpha = 1) + geom_smooth(aes(y = BGN), color = "green") + theme_classic() + ridgetheme +scale_y_continuous(limits = c(1,5)) + scale_color_manual(values = colors_list) + guides(colour = guide_legend(override.aes = list(size=10))))


df <- FetchData(ds2_PA,vars = c("FB_score1","SMC_score1","BGN","LUM","UMAP_1","UMAP_2"))
# df <- arrange(df,FB_score1,by_group = F)
data <- cbind(df,index = 1:nrow(df),cluster = Idents(ds2_PA))

ggplot(data,aes(x=SMC_score1)) + geom_point(aes(y = BGN, color = cluster),alpha = 1) + geom_smooth(aes(y = BGN), color = "red") + theme_classic() + ridgetheme + scale_y_continuous(limits = c(1,5)) + scale_color_manual(values = colors_list) + guides(colour = guide_legend(override.aes = list(size=10)))

ggplot(data,aes(x=FB_score1)) + geom_point(aes(y = LUM, color = cluster),alpha = 1) + geom_smooth(aes(y = BGN), color = "green") + theme_classic() + ridgetheme +scale_y_continuous(limits = c(1,5)) + scale_color_manual(values = colors_list) + guides(colour = guide_legend(override.aes = list(size=10)))

# ggplot(data,aes(x=FB_score1)) + geom_point(aes(y = BGN),color = "#e2b398",alpha = 1) + geom_smooth(aes(y = BGN), color = "red") + geom_point(aes(y=LUM),color = "#d1eba8",alpha = 1) + geom_smooth(aes(y = LUM), color = "green") + theme_classic() + ridgetheme +scale_y_continuous(limits = c(1,5))

fig.D

AC->PA

confuse_mat <- XGBoost_predict_from_seuobj(ds2_PA, bst_model, return_confuse_matrix = T)

Warning in XGBoost_predict_from_seuobj(ds2_PA, bst_model, return_confuse_matrix = T) :
  强制改变过程中产生了NA
Warning in XGBoost_predict_from_seuobj(ds2_PA, bst_model, return_confuse_matrix = T) :
  Please ensure that seurat idents are in numeric forms

[1] "ARI = 0.682835569069112"

confuse_mat <- XGBoost_predict_from_seuobj(ds2_PA, bst_model, return_confuse_matrix = T)

Warning in XGBoost_predict_from_seuobj(ds2_PA, bst_model, return_confuse_matrix = T) :
  强制改变过程中产生了NA
Warning in XGBoost_predict_from_seuobj(ds2_PA, bst_model, return_confuse_matrix = T) :
  Please ensure that seurat idents are in numeric forms

[1] "ARI = 0.682835569069112"

sankey_plot(confuse_mat,session = "AC -> PA")

numerical umap

embedding <- FetchData(object = ds2_PA, vars = c("UMAP_1", "UMAP_2"))
embedding <- cbind(embedding, cbind(ds2_PA$X0,ds2_PA$X1,ds2_PA$X2,ds2_PA$X3))

ggobj <- ggplot() +
  geom_point(data = embedding[embedding$`1`>0.1,], 
             aes(x = UMAP_1, y = UMAP_2, color = `1`), shape=16, size = 3, alpha=0.5) + 
  scale_color_gradient('0', low = "#FFFFFF00", high = "#6dc0a6") +
  new_scale("color") +
    geom_point(data = embedding[embedding$`2`>0.1,], 
             aes(x = UMAP_1, y = UMAP_2, color = `2`),shape=16, size = 3, alpha=0.5) + 
  scale_color_gradient('1', low = "#FFFFFF00", high = "#e2b398") +
   new_scale("color") +
    geom_point(data = embedding[embedding$`3`>0.1,], 
             aes(x = UMAP_1, y = UMAP_2, color = `3`),shape=16, size = 3, alpha=0.5) + 
  scale_color_gradient('2', low = "#FFFFFF00", high = "#e2a2ca") +
  new_scale("color") +
    geom_point(data = embedding[embedding$`4`>0.1,], 
             aes(x = UMAP_1, y = UMAP_2, color = `4`),shape=16, size = 3, alpha=0.5) + 
  scale_color_gradient('3', low = "#FFFFFF00", high = "#d1eba8") +
        xlab("UMAP 1") + ylab("UMAP 2")  +
        theme(axis.line = element_line(arrow = arrow(length = unit(0.2, "cm")))) +
        scale_y_continuous(breaks = NULL) +
        scale_x_continuous(breaks = NULL) + 
  theme(panel.background = element_blank(), panel.grid = element_blank(), legend.position = "bottom")
ggsave("./fig2/sup_ds2PA_umap.png",device = png, plot = ggobj, height = 10,width = 10)

PA->AC

train on PA

numerical umap

ggsave("./fig2/sup_ds2AC_umap.png",device = png, plot = ggobj,height = 10,width = 10)

Error in FUN(X[[i]], ...) : 找不到对象'0'

GSVA

GSVAres <- readRDS("GSVAres.rds")
es <- data.frame(t(GSVAres),stringsAsFactors=F)  #可视化相关通路的在umap上聚集情况
ds2_AC <- AddMetaData(ds2_AC, es)
f("CUI_TCF21_TARGETS_UP", label = F, ds2_AC) +scale_colour_gradient(low="#1E90FF", high="#ff2121")

ds2_PA <- AddMetaData(ds2_PA, es)
f("CUI_TCF21_TARGETS_UP", label = F, ds2_PA) +scale_colour_gradient(low="#1E90FF", high="#ff2121")

Add a new chunk by clicking the Insert Chunk button on the toolbar or by pressing Ctrl+Alt+I.

When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the Preview button or press Ctrl+Shift+K to preview the HTML file).

The preview shows you a rendered HTML copy of the contents of the editor. Consequently, unlike Knit, Preview does not run any R code chunks. Instead, the output of the chunk when it was last run in the editor is displayed.